booktitle = "Proceedings of the 24th {ACM} International on
Conference on Information and Knowledge Management,
{CIKM}",

year = "2015",

pages = "83--92",

address = "Melbourne, Australia",

month = oct # " 19 - 23",

keywords = "genetic algorithms, genetic programming",

bibsource = "dblp computer science bibliography, http://dblp.org",

biburl = "http://dblp.uni-trier.de/rec/bib/conf/cikm/MunozTG15",

timestamp = "Thu, 12 Nov 2015 16:33:35 +0100",

URL = "http://doi.acm.org/10.1145/2806416.2806478",

DOI = "doi:10.1145/2806416.2806478",

abstract = "This paper presents an approach to combine rank
aggregation techniques using a soft computing technique
-- Genetic Programming -- in order to improve the
results in Information Retrieval tasks. Previous work
shows that by combining rank aggregation techniques in
an agglomerative way, it is possible to get better
results than with individual methods. However, these
works either combine only a small set of lists or are
performed in a completely ad-hoc way. Therefore, given
a set of ranked lists and a set of rank aggregation
techniques, we propose to use a supervised genetic
programming approach to search combinations of them
that maximize effectiveness in large search spaces.
Experimental results conducted using four datasets with
different properties show that our proposed approach
reaches top performance in most datasets. Moreover,
this cross-dataset performance is not matched by any
other baseline among the many we experiment with, some
being the state-of-the-art in learning-to-rank and in
the supervised rank aggregation tasks. We also show
that our proposed framework is very efficient,
flexible, and scalable.",